Instructions to use gszabo/large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gszabo/large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gszabo/large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gszabo/large") model = AutoModelForMaskedLM.from_pretrained("gszabo/large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gszabo/large: direct link, hf CLI and curl.
- Browser
- Download file 1.59 GB
-
https://huggingface.co/gszabo/large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gszabo/large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gszabo/large/resolve/main/pytorch_model.bin
1.59 GB
- Xet hash:
- 114b5acb83b909d052f1e18974c4bd52fa0a972c2e7072dd4d64d4d6a4f9d277
- Size of remote file:
- 1.59 GB
- SHA256:
- a80b09bc0877cb5263fd17d9a2b05746307f5b6b70d35768b6e34c73ed41f5dd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.